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Measurementsfinal
0-0,0047254554725455
1-0,106630909106630909
2-0,07416909174169091
30,208947273208947273
4-0,05670545556705455
5-0,175458182175458182
60,05177454551774545
70,04150181841501818
8-0,02444909124449091
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10-0,165185455165185455
110,02280545522805455
120,468641818468641818
130,242230909242230909
140,01972363619723636
15-0,01088909110889091
16-0,125943636125943636
17-0,04150181841501818
180,267090909267090909
19-0,07416909174169091
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" + ], + "text/plain": [ + " Measurements final\n", + "0 -0,004725455 4725455\n", + "1 -0,106630909 106630909\n", + "2 -0,074169091 74169091\n", + "3 0,208947273 208947273\n", + "4 -0,056705455 56705455\n", + "5 -0,175458182 175458182\n", + "6 0,051774545 51774545\n", + "7 0,041501818 41501818\n", + "8 -0,024449091 24449091\n", + "9 0,167445455 167445455\n", + "10 -0,165185455 165185455\n", + "11 0,022805455 22805455\n", + "12 0,468641818 468641818\n", + "13 0,242230909 242230909\n", + "14 0,019723636 19723636\n", + "15 -0,010889091 10889091\n", + "16 -0,125943636 125943636\n", + "17 -0,041501818 41501818\n", + "18 0,267090909 267090909\n", + "19 -0,074169091 74169091" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "df1['lable']=[0]*999" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "df1=df1.drop('Measurements',axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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finallable
047254550
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2741690910
32089472730
4567054550
51754581820
6517745450
7415018180
8244490910
91674454550
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" + ], + "text/plain": [ + " final lable\n", + "0 4725455 0\n", + "1 106630909 0\n", + "2 74169091 0\n", + "3 208947273 0\n", + "4 56705455 0\n", + "5 175458182 0\n", + "6 51774545 0\n", + "7 41501818 0\n", + "8 24449091 0\n", + "9 167445455 0\n", + "10 165185455 0\n", + "11 22805455 0\n", + "12 468641818 0\n", + "13 242230909 0\n", + "14 19723636 0\n", + "15 10889091 0\n", + "16 125943636 0\n", + "17 41501818 0\n", + "18 267090909 0\n", + "19 74169091 0" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "a2=[]\n", + "a3=[]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(999):\n", + " i1=df2['Measurement'][i]\n", + " before_comma, after_comma = i1.split(',')\n", + " before_comma = int(before_comma)\n", + " after_comma = int(after_comma)\n", + " a2.append(before_comma)\n", + " a3.append(after_comma)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "df2['final']=a3" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "df2['lable']=1" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "df2=df2.drop('Measurement',axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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finallable
0977963641
1548563641
2369818181
3544454551
4211618181
536981821
6106836361
729381
81045763641
91358054551
101245054551
1161021
1236981821
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1449309091
1549309091
1655472731
17131490911
1834927271
19482818181
\n", + "
" + ], + "text/plain": [ + " final lable\n", + "0 97796364 1\n", + "1 54856364 1\n", + "2 36981818 1\n", + "3 54445455 1\n", + "4 21161818 1\n", + "5 3698182 1\n", + "6 10683636 1\n", + "7 2938 1\n", + "8 104576364 1\n", + "9 135805455 1\n", + "10 124505455 1\n", + "11 6102 1\n", + "12 3698182 1\n", + "13 27736364 1\n", + "14 4930909 1\n", + "15 4930909 1\n", + "16 5547273 1\n", + "17 13149091 1\n", + "18 3492727 1\n", + "19 48281818 1" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.concat([df1, df2], axis=0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1998, 2)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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finallable
047254550
11066309090
2741690910
32089472730
4567054550
51754581820
6517745450
7415018180
8244490910
91674454550
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11228054550
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14197236360
15108890910
161259436360
17415018180
182670909090
19741690910
\n", + "
" + ], + "text/plain": [ + " final lable\n", + "0 4725455 0\n", + "1 106630909 0\n", + "2 74169091 0\n", + "3 208947273 0\n", + "4 56705455 0\n", + "5 175458182 0\n", + "6 51774545 0\n", + "7 41501818 0\n", + "8 24449091 0\n", + "9 167445455 0\n", + "10 165185455 0\n", + "11 22805455 0\n", + "12 468641818 0\n", + "13 242230909 0\n", + "14 19723636 0\n", + "15 10889091 0\n", + "16 125943636 0\n", + "17 41501818 0\n", + "18 267090909 0\n", + "19 74169091 0" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 999\n", + "1 999\n", + "Name: lable, dtype: int64" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['lable'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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finallable
count1.998000e+031998.000000
mean1.177357e+080.500000
std1.288797e+080.500125
min1.130000e+020.000000
25%2.660636e+070.000000
50%7.560727e+070.500000
75%1.611277e+081.000000
max8.442127e+081.000000
\n", + "
" + ], + "text/plain": [ + " final lable\n", + "count 1.998000e+03 1998.000000\n", + "mean 1.177357e+08 0.500000\n", + "std 1.288797e+08 0.500125\n", + "min 1.130000e+02 0.000000\n", + "25% 2.660636e+07 0.000000\n", + "50% 7.560727e+07 0.500000\n", + "75% 1.611277e+08 1.000000\n", + "max 8.442127e+08 1.000000" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Int64Index: 1998 entries, 0 to 998\n", + "Data columns (total 2 columns):\n", + " # Column Non-Null Count Dtype\n", + "--- ------ -------------- -----\n", + " 0 final 1998 non-null int64\n", + " 1 lable 1998 non-null int64\n", + "dtypes: int64(2)\n", + "memory usage: 46.8 KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "X_train,X_test,y_train,y_test=train_test_split(df['final'],df['lable'],test_size=0.15)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "X_train1=np.array(X_train).reshape(-1,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "X_test1=np.array(X_test).reshape(-1,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import accuracy_score, f1_score" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "def score(X_test,model):\n", + " model.fit(X_train1,y_train)\n", + " y_pred=model.predict(X_test)\n", + " a=accuracy_score(y_test,y_pred)*100\n", + " return a" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.ensemble import AdaBoostClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "52.0\n" + ] + } + ], + "source": [ + "model = LogisticRegression(random_state=0,solver='saga',penalty='l1',max_iter=10000)\n", + "v=score(X_test1,model)\n", + "print(v)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "75.66666666666667" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adaboost = AdaBoostClassifier(n_estimators=1000, random_state=0)\n", + "score(X_test1,adaboost)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "73.0\n" + ] + } + ], + "source": [ + "gnb = GaussianNB()\n", + "v=score(X_test1,gnb)\n", + "print(v)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "74.33333333333333\n" + ] + } + ], + "source": [ + "random_forest = RandomForestClassifier(n_estimators=1000, max_depth=10, random_state=0)\n", + "v=score(X_test1,random_forest)\n", + "print(v)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: xgboost in c:\\users\\ysach\\anaconda3\\lib\\site-packages (2.0.3)\n", + "Requirement already satisfied: scipy in c:\\users\\ysach\\anaconda3\\lib\\site-packages (from xgboost) (1.10.0)\n", + "Requirement already satisfied: numpy in c:\\users\\ysach\\anaconda3\\lib\\site-packages (from xgboost) (1.23.5)\n" + ] + } + ], + "source": [ + "!pip install xgboost" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.77\n" + ] + } + ], + "source": [ + "from xgboost import XGBClassifier\n", + "model =XGBClassifier(\n", + " objective='binary:logistic',\n", + " eval_metric='auc',\n", + " learning_rate=0.05,\n", + " max_depth=10,\n", + " n_estimators=1000,\n", + " reg_alpha=3\n", + ")\n", + "model.fit(X_train1, y_train)\n", + "y_pred=model.predict(X_test1)\n", + "print(accuracy_score(y_test,y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=[10,5])\n", + "bars=plt.bar(['LogisticRegression','adaboost','GaussianNB','random_forest','XGBClassifier'],[52.0,75.67,73.0,74.33,77])\n", + "for bar in bars:\n", + " yval = bar.get_height()\n", + " plt.text(bar.get_x() + bar.get_width()/2, yval + 1, round(yval, 2), ha='center', va='bottom')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Bearing Classification/README.md b/Bearing Classification/README.md new file mode 100644 index 000000000..d52798b7b --- /dev/null +++ b/Bearing Classification/README.md @@ -0,0 +1,70 @@ +# Bearing Classification using ML + +## PROJECT TITLE + +Bearing Classification + +## GOAL + +To identify faulty and healthy bearing. + +## DATASET + +The link for the dataset used in this project: https://www.kaggle.com/datasets/zlemglsmklkaya/healthy-vs-faulty-bearings/data?select=Healthy-bearing.csv + +## EDA: +![Alt text](Images/Input_Dataset.png) +![Alt text](Images/EDA1.png) +Shape: (1998,2) + +## DESCRIPTION + +This project aims to identify the faulty and helthy bearings. + +## WHAT I HAD DONE + +1. Data collection: From the link of the dataset given above. +2. Data preprocessing: Preprocessed the data to create valid features. +3. Model selection: XGBC,Random Forest,Logestic Regression,Gaussian Bayes,AdaBoost Classifier. +4. Comparative analysis: Compared the accuracy score of all the models. + + +## MODELS SUMMARY + +- XGBC +- Logistic Regression +- Adaboost Classifier +- Random Forest Classifier +- Gaussian Bayes + +## LIBRARIES NEEDED + +The following libraries are required to run this project: + +- matplotlib +- numpy +- pandas +- sklearn + +## EVALUATION METRICS + +The evaluation metrics I used to assess the models: + +- Accuracy + +It is shown using Confusion Matrix in the Images folder + +## RESULTS +Results on Val dataset: +XGBC: 77.33% +Random Forest: 77.67% +Adaboost: 75% +Logistic Regression: 74% +Gaussian Bayes: 73.33% +DTC:77.33% +![Alt text](Images/Metrics.png) + +## CONCLUSION +Based on results we can draw following conclusions: + +1.The Random Forest worked the best \ No newline at end of file